Dataset Information

Genotype-Based Bayesian Analysis of Gene-Environment Interactions with Multiple Genetic Markers and Misclassification in Environmental Factors.

ABSTRACT: A key component to understanding etiology of complex diseases, such as cancer, diabetes, alcohol dependence, is to investigate gene-environment interactions. This work is motivated by the following two concerns in the analysis of gene-environment interactions. First, multiple genetic markers in moderate linkage disequilibrium may be involved in susceptibility to a complex disease. Second, environmental factors may be subject to misclassification. We develop a genotype based Bayesian pseudolikelihood approach that accommodates linkage disequilibrium in genetic markers and misclassification in environmental factors. Since our approach is genotype based, it allows the observed genetic information to enter the model directly thus eliminating the need to infer haplotype phase and simplifying computations. Bayesian approach allows shrinking parameter estimates towards prior distribution to improve estimation and inference when environmental factors are subject to misclassification. Simulation experiments demonstrated that our method produced parameter estimates that are nearly unbiased even for small sample sizes. An application of our method is illustrated using a case-control study of interaction between early onset of drinking and genes involved in dopamine pathway.

SUBMITTER: Lobach I

PROVIDER: S-EPMC4500528 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Genotype-Based Bayesian Analysis of Gene-Environment Interactions with Multiple Genetic Markers and Misclassification in Environmental Factors.

Lobach Iryna I Fan Ruzong R

Journal of probability and statistics 20120101

A key component to understanding etiology of complex diseases, such as cancer, diabetes, alcohol dependence, is to investigate gene-environment interactions. This work is motivated by the following two concerns in the analysis of gene-environment interactions. First, multiple genetic markers in moderate linkage disequilibrium may be involved in susceptibility to a complex disease. Second, environmental factors may be subject to misclassification. We develop a genotype based Bayesian pseudolikeli ...[more]

PMID: 26180529

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Project description:Unraveling the molecular processes that lead from genotype to phenotype is crucial for the understanding and effective treatment of genetic diseases. Knowledge of the causative genetic defect most often does not enable treatment; therefore, causal intermediates between genotype and phenotype constitute valuable candidates for molecular intervention points that can be therapeutically targeted. Mapping genetic determinants of gene expression levels (also known as expression quantitative trait loci or eQTL studies) is frequently used for this purpose, yet distinguishing causation from correlation remains a significant challenge. Here, we address this challenge using extensive, multi-environment gene expression and fitness profiling of hundreds of genetically diverse yeast strains, in order to identify truly causal intermediate genes that condition fitness in a given environment. Using functional genomics assays, we show that the predictive power of eQTL studies for inferring causal intermediate genes is poor unless performed across multiple environments. Surprisingly, although the effects of genotype on fitness depended strongly on environment, causal intermediates could be most reliably predicted from genetic effects on expression present in all environments. Our results indicate a mechanism explaining this apparent paradox, whereby immediate molecular consequences of genetic variation are shared across environments, and environment-dependent phenotypic effects result from downstream integration of environmental signals. We developed a statistical model to predict causal intermediates that leverages this insight, yielding over 400 transcripts, for the majority of which we experimentally validated their role in conditioning fitness. Our findings have implications for the design and analysis of clinical omics studies aimed at discovering personalized targets for molecular intervention, suggesting that inferring causation in a single cellular context can benefit from molecular profiling in multiple contexts.

Project description:Major depressive disorder (MDD) is the most common psychiatric disorder and the second overall cause of disability. Even though a significant amount of the variance in the MDD phenotype is explained by inheritance, specific genetic variants conferring susceptibility to MDD explain only a minimal proportion of MDD causality. Moreover, genome-wide association studies have only identified two small-sized effect loci that reach genome-wide significance. In this study, a group of Mexican-American patients with MDD and controls recruited for a pharmacogenetic study were genotyped for nonsynonymous single-nucleotide polymorphisms (nsSNPs) and used to explore the interactions of multiple functional genetic variants with risk-classification tree analysis. The risk-classification tree analysis model and linkage disequilibrium blocks were used to replicate exploratory findings in the database of genotypes and phenotypes (dbGaP) for major depression, and pathway analysis was performed to explore potential biological mechanisms using the branching events. In exploratory analyses, we found that risk-classification tree analysis, using 15 nsSNPs that had a nominal association with MDD diagnosis, identified multiple increased-MDD genotype clusters and significant additive interactions in combinations of genotype variants that were significantly associated with MDD. The results in the dbGaP for major depression disclosed a multidimensional dependent phenotype constituted of MDD plus significant modifiers (smoking, marriage status, age, alcohol abuse/dependence and gender), which then was used for the association tree analysis. The reconstructed tree analysis for the dbGaP data showed robust reliability and replicated most of the genes involved in the branching process found in our exploratory analyses. Pathway analysis using all six major events of branching (PSMD9, HSD3B1, BDNF, GHRHR, PDE6C and PDLIM5) was significant for positive regulation of cellular and biological processes that are relevant to growth and organ development. Our findings not only provide important insights into the biological pathways underlying innate susceptibility to MDD but also offer a predictive framework based on interactions of multiple functional genetic variants and environmental factors. These findings identify novel targets for therapeutics and for translation into preventive, clinical and personalized health care.

Dataset Information

Genotype-Based Bayesian Analysis of Gene-Environment Interactions with Multiple Genetic Markers and Misclassification in Environmental Factors.

Publications

Genotype-Based Bayesian Analysis of Gene-Environment Interactions with Multiple Genetic Markers and Misclassification in Environmental Factors.

Similar Datasets

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